Learning for Interval Prediction of Electricity Demand: A Cluster-based Bootstrapping Approach
This work addresses the need for reliable uncertainty quantification in electricity demand forecasting for microgrid operators, though it is incremental as it builds on existing bootstrapping techniques with clustering.
The paper tackles the problem of predicting day-ahead electricity demand intervals for microgrids, where high stochasticity makes point estimates error-prone, and introduces a cluster-based bootstrapping approach that groups similar days to improve interval accuracy, achieving competitive performance on real data compared to other methods.
Accurate predictions of electricity demands are necessary for managing operations in a small aggregation load setting like a Microgrid. Due to low aggregation, the electricity demands can be highly stochastic and point estimates would lead to inflated errors. Interval estimation in this scenario, would provide a range of values within which the future values might lie and helps quantify the errors around the point estimates. This paper introduces a residual bootstrap algorithm to generate interval estimates of day-ahead electricity demand. A machine learning algorithm is used to obtain the point estimates of electricity demand and respective residuals on the training set. The obtained residuals are stored in memory and the memory is further partitioned. Days with similar demand patterns are grouped in clusters using an unsupervised learning algorithm and these clusters are used to partition the memory. The point estimates for test day are used to find the closest cluster of similar days and the residuals are bootstrapped from the chosen cluster. This algorithm is evaluated on the real electricity demand data from EULR(End Use Load Research) and is compared to other bootstrapping methods for varying confidence intervals.